Data Science Class 10 Syllabus
The syllabus consists of five units: (i) Use of statistics in Data Science (ii) Distributions in Data Science (iii) Identifying Patterns (v) Data Merging (v) Ethics in Data Science.
1. Use of statistics in Data Science
- What are subsets?
- Two-way frequency table
- Interpreting two-way tables
- Two-way relative frequency table
- Meaning of mean
- Mean Absolute Deviation
- What is Standard Deviation?
2. Distributions in Data Science
- What is distribution in data science?
- What are different types of distributions?
- Statistical Problem Solving Process
3. Identifying Patterns
- What is partiality, preference and prejudice?
- How to identify the partiality, preference and prejudice?
- Probability for Statistics
- The Central Limit Theorem
- Why is the Central Limit Theorem important?
4. Data Merging
- Overview of Data Merging
- What is Z-Score?
- How to calculate a Z-score?
- How to interpret the Z-score?
- Why is a Z-score so important?
- Concept of Percentiles
5. Ethics in Data Science
- Note about data governance framework
- Ethical guidelines around data analysis
- Discarding the Data